The arrival of the programming language R has narrowed the scope of other programming languages since it is widely preferred by most data scientists & researchers and statisticians. R came to view in late 1993 and is a GNU package that empowers statistical computing. Over the past few years, R's popularity has grown manifold, especially in Data analytics field. It's an era of data science, and business analytics is a cornerstone of it. Competition is growing like never before and one cannot afford to lose dollars in lieu of using a wrong tool.
With the availability of so many tools, techies, especially beginners can be confused with which programming tool to opt for. If you are miring yourself in finding the best programming language, stay tuned to this post and get to know why R programming is the lynchpin of data science.
R is for Non-Technicals
Sorting through high-end data science tools will introduce you to the top two tools R or Python. Software engineers with knowledge of math, stats, and Machine Learning prefer Python to any other language, but the problem arises when a developer needs library support for subjects such as Econometrics. Considering the non-technical background of most data science specialists, learning Python is one of the significant challenges for them. Moreover, weaker support of Python for Econometrics which is essential for businesses and finances for communication in form of reports adds one more point to its limitations. In the light of the above points, it is clear that Python is not a reliable solution, and we need to consider the next option-R.
R is used for statistical programming. It supports ML, Stats, and data science libraries to streamline your programming. R and data science share a good relationship that ultimately helps business as the language supports topic-specific packages and, moreover, the infrastructure of its communication is highly specific. Therefore, data scientists show a great interest in R as its libraries support Finance, Econometrics, etc., which makes sense for business analytics.
Tidyverse is a Savior of R
The birth of R brings along its complexity. As structuring and formality were not the first concern in the beginning of programming, R was considered highly inconsistent to learn. However, the advent of Tidyverse changed the scenario completely. Being a pack of packages and tools, Tidyverse provides you with a consistent structural programming interface. Moreover, dplyr and ggplot2 has reduced learning curve complexities greatly. Today, R has achieved the highest level of consistency, all thanks in part to the evolving nature of R. From visualization to iteration, to manipulation, Vidyverse supports everything, which makes R an easy language to learn.
R can Kickstart Business
What attracts data scientists towards R is its potential for providing business with ready reports and infographics, and ML powered web development. No other language can stand in front R on the grounds of ease and effectiveness. Let's take an example of RMARKDOWN and shiny. RMARKDOWN is a framework, which enables you to create reconstructable reports to build blogs, presentations, websites, books journals, etc. Organizations embrace this tool not only to prepare a business analytics report but also commercialize what this framework provides them with. Shiny is an R empowered framework that helps you create interactive web applications. It is a handy tool, and companies rely on it to achieve web development goals.
Higher Bandwidth for Libraries
R is a powerful business infrastructure that Excel on Steroids from a business perspective. It is capable of implementing various algorithms, for example, high-end Machine learning package (H2O), TensorFlow deep learning packages, xgboost the top Kaggle algorithm, etc., that is probably not possible for other languages to do. Tidyverse is the backbone of the language R. It structural approach triggers consistency during application development no matter how much complex it is. It encompasses an array of libraries like dpylr, tidyr, stringr, lubridate, forecast, etc., that takes development to the next level.
Having a great community support is highly critical for any Programming language or interface to excel. R has a huge fan following due to tech enthusiasts that learn and provide beginners with the latest updates. The data science field has already acknowledged the importance of R for developing exclusive reports and streamlining communication.
Thus, we can see how R programming language is influencing modern development trends in data science. Complex business operations from a critical decision making to process optimization are achieved through the collected data from the historical data sets. R with the vast number of packages allows us to accomplish advanced computing tasks like regression, classification, and other scientific computations in just a few minutes. These algorithms lead to accurate results for predictions, modeling, pattern analysis, graphical or statistical representation. Maybe these cool features are the main reason behind the popular friendship between R and data science.